Systems and methods for performing scoring optimization

ABSTRACT

The invention provides systems and methods relating to generating a unified determination based on subdetermination, and in particular, generating a unified score based on respective scores. For example, the invention provides a method for generating a unified determination based on subdeterminations, the method including generating a first subdetermination based on first criteria; generating a second subdetermination based on second criteria; and generating a unified determination based on the first subdetermination and the second subdetermination. The generation of the unified determination includes (a) assigning, using iterative processing, an assigned weighting respectively to the first determination and second determination; (b) determining if the assigned weighting satisfies at least one constraint; (c) comparing the assigned weighting to an optimized weighting, which was previously determined, to determine if the assigned weighting is improved over the optimized weighting; and (d) if the assigned weighting is improved, then assigning the assigned weighting to be the optimized weighting.

BACKGROUND OF THE INVENTION

There are a wide variety of situations where determinations are made atdifferent points through the course of a process and/or wheredeterminations are made based on different data. Such determinations mayvary by the particular type of data that is used in the determination.Alternatively, such determinations may involve data that is secured at adifferent time, i.e., updated data may be used (instead of older datathat was used in a prior determination). A determination might beexpressed in terms of a score, i.e., some other quantitativerepresentation.

As can be appreciated, such different determinations made over a periodof time or made based on different data may vary in the result suchdeterminations yield. For example, a credit card issuer may beconducting a campaign to secure new credit card customers. The campaignmight typically involve determining individuals that should be mailedcredit card offers. In determining such individuals, the credit cardissuer generates a credit risk score for each individual. The creditrisk score may be based on data secured from a credit bureau or otherdata that is assessable by the credit card issuer. At this point in theprocess, the credit risk score might be characterized as a “front end”risk score. In other words, at actual mailing selection time, the creditcard issuer has to select names for offers from the whole crediteligible universe.

Individuals who receive the offer (through mailings, e-mailings, or anyother suitable medium) have the opportunity to review and accept theoffer. Accordingly, at some later time, the credit card issuer willreceive responses from some of those individuals.

Once a response is received from an individual (a respondent), thecredit card issuer then determines whether the credit card issuer willindeed issue a credit card to the respondent. In other words, at creditapproval/decline time, the business has to make the booking decisionamong all of respondent applicants. This decision involves determinationof a further risk score, i.e., a “back-end” risk. The back-end riskscore will thus be determined at a later time, than the front endrisk-score, and might also involve different parameters. As a result, itis very likely the back-end risk score is different from the one basedon the random sample of the whole eligible credit universe, i.e.,different from the front end risk score.

In such situation, the credit card issuer, as between the front-end riskscore and the back-end risk score, has two different universes and twodifferent goals. Using known techniques, it is very difficult to providesatisfactory results for one goal while it is developed against anothergoal. Historically business uses two different scores, one for the frontend determination and one for the back end determination. However, thatapproach sometimes causes problems since the credit card issuer or otherbusiness makes the selection decision to mail an offer based on onescore, and later the business decides to decline a responder of theoffer based on second score.

Such action is unfortunately sometimes necessary, from a businessperspective, but is not beneficial to the business from a publicrelations perspective.

The above and other problems are present in known processes.

BRIEF SUMMARY OF THE INVENTION

The invention provides systems and methods relating to generating aunified determination based on subdeterminations, and in particular,generating a unified score based on respective scores. For example, theinvention provides a method for generating a unified determination basedon subdeterminations, the method including generating a firstsubdetermination based on first criteria; generating a secondsubdetermination based on second criteria; and generating a unifieddetermination based on the first subdetermination and the secondsubdetermination. The generation of the unified determination includes(a) assigning, using iterative processing, an assigned weightingrespectively to the first determination and second determination; (b)determining if the assigned weighting satisfies at least one constraint;(c) comparing the assigned weighting to an optimized weighting, whichwas previously determined, to determine if the assigned weighting isimproved over the optimized weighting; and (d) if the assigned weightingis improved, then assigning the assigned weighting to be the optimizedweighting.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention can be more fully understood by reading thefollowing detailed description together with the accompanying drawings,in which like reference indicators are used to designate like elements,and in which:

FIG. 1 is a flowchart showing a process of building a unified score forboth front end mail selection and back end credit decision in accordancewith one embodiment of the invention;

FIG. 2 is a flow chart showing further details of the “generatecombination score using optimization process” step of FIG. 1 inaccordance with one embodiment of the invention;

FIG. 3 is a block diagram showing a score processing system inaccordance with one embodiment of the invention;

FIG. 4 is a block diagram showing the combination score processor ofFIG. 3 in further detail in accordance with one embodiment of theinvention;

FIG. 5 is a flowchart showing a process of building a unifieddetermination based on subdeterminations in accordance with oneembodiment of the invention; and

FIG. 6 is a flow chart showing further details of the “generate theunified determination (based on the first subdetermination and thesecond subdetermination) using an optimization process” step of FIG. 5in accordance with one embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, aspects of the systems and methods in accordance withvarious embodiments of the invention will be described. As used herein,any term in the singular may be interpreted to be in the plural, andalternatively, any term in the plural may be interpreted to be in thesingular.

The systems and methods of the invention are directed to the abovestated problems, as well as other problems, that are present inconventional techniques.

In introduction, the invention, in one embodiment, provides a system andmethod to perform risk based scoring. For example, the invention may beapplied to the situation where risk scores are needed both in (1)selection of recipients for mailings; and (2) the decision whether tobook applicants responding to such mailings, i.e., a risk score isneeded that is representative of both the “front end” and “back end”risk assessment criteria.

As further background information in accordance with one embodiment ofthe invention, historically a business uses two different scores, onefor the front end risk assessment and one for the back end riskassessment. That approach causes problems in the situation that (1) abusiness makes a selection decision to mail an offer based on one score,and later, (2) the business decides to decline a responder of the offerbased on second score. The invention provides a single score to overcomethis problem.

The invention provides a novel approach that generates a score that maybe used for both front end and back end risk assessment. In a method ofone embodiment of the invention, a front end initial score and a backend initial score are developed using data obtained from a creditbureau, for example. The two initial scores are developed based onrespective criteria relating to the front and back end risk assessments.Then, the two initial scores are combined into a single score. Thiscombining is performed using an optimization process. The goal is thatthe single score resulting from the combination should satisfy appliedconstraints and outperform benchmark scores, so as to maximize the totalperformance measurement.

In accordance with one embodiment of the invention, the optimizationprocess (by which the initial front end score and the initial back endscore are combined) may utilize Kolmogorov-Smirnov test (KS-test)processing. The KS-test is typically used to determine the magnitudethat two data sets differ. In the invention, the KS-test may be utilizedin iterative processing to determine a best parameter to use incombining the two initial scores. In particular, the KS-test may be usedto determine a weighting parameter based on respective objectives of thefront end initial score and a back end initial score, and predeterminedconstraints. The predetermined constraints may be based on performanceof benchmark scores and business requirements, for example. As notedbelow, other statistical methodologies may be used in lieu of the KSTest.

The invention as described herein may be applied to the selection ofrecipients for mailings and the booking of the respondents to suchmailings. Accordingly, the system and method of the invention mayprovide a key tool in acquisition mailing campaigns. In addition, theinvention may be utilized in a wide variety of other situations todevelop models—to achieve satisfactory results for multiple goals at thesame time.

Hereinafter, further aspects of the systems and methods of embodimentswill be described in further detail.

Accordingly, in one embodiment, the invention addresses the need todevelop credit risk score for new account acquisition campaigns, and inparticular in the situation where a business requires a single score forboth front end processing (in selection of persons to send offers) andback end processing (in the credit approval or decline processing).

In this situation, at actual mailing selection time, a business has toselect names, to which offers will be sent, from the whole crediteligible universe. At this stage of the process, a business may use thecriteria of whether a prospect will go bad on any trade with a financialinstitution as objective function for the risk score, i.e., thefront-end risk score is predicting the possibility of whether theparticular individual will have at least one bad trade with anyfinancial institution in next several months, i.e., such as 12 months,for example.

However, at credit approval or decline time, the particular business hasto make the booking decision among all of responder applicants. Thisresponder population has to become the development population for theback-end risk score and very likely it is very different from one basedon the random sample of whole eligible credit universe. Furthermore forthis back-end risk score, what the known processing predicts, forexample, is whether or not an approved account with the offeringinstitution will be defaulted in the future, i.e., if the institutionindeed decides to approve and book the account.

Accordingly, it should be appreciated that the business, i.e., theoffering institution, clearly has two different universes and twodifferent objective functions. Using known techniques, it is verydifficult to provide satisfactory results for one goal while it isdeveloped against another goal, as is noted above. In particular, thereis no easy statistical methodology to achieve both goals at same time.As should be appreciated, if the back end score provides differentindicators, e.g. such as the credit worthiness of the individual, ascompared to the front end score, the back end score may indicate to notextend the credit card offer. Such is problematic in disappointing theindividual, who had been offered the card, and thus bad from a publicrelations perspective.

In order to solve the above conflict between two scores, the inventionprovides a unified score to represent both the traditional front endscore and the traditional back end. The invention provides the unifiedscore in what is believed to be a very novel approach. In a first step,each score is generated based on its own population and objectivefunction. In accordance with one embodiment of the invention, each scoreis generated with the same set of bureau variables and bureau data.

In a second phase of the process, an optimization process is used tocombine the two scores into a final product, i.e., a unified score. Onegoal is that the final score should outperform the individual benchmarkscores on its own population, but at the same time maximize the totalperformance measurement.

In accordance with one embodiment of the invention, FIG. 1 is aflowchart showing a process of building a unified score for both frontend mail selection and back end credit decision. As shown, the processstarts in step 100 and passes to steps 112 and 114. That is, steps 112and 114 are performed in parallel.

In step 112, a first objective is identified. In this example, the firstobjective is to predict whether the particular individual is a bad riskbased on credit bureau data, i.e., so as to determine whether theparticular individual is a good candidate to forward an offer. The firstobjective might be thought of as determining the merits of theindividual and their credit risk vis-à-vis the individuals existingcreditors. Accordingly, from step 112, the process passes to step 122.in step 122, the front end risk score, score_1 is determined. In otherwords, score_1 assesses available candidates in the credit eligibleuniverse to determine which candidates should be mailed, or otherwiseforwarded offers. After step 122, the process passes to step 130.

In parallel to steps 112 and 122, in this embodiment, the process ofFIG. 1 includes step 114. In step 114, an objective is determined topredict an internal bad determination, i.e., from the perspective of thebank considering issuance of the credit offer to the individual.Accordingly, in step 124, what is herein characterized as the back endrisk score (score_2) is determined. Score_2 is a score assessingrespondents from the responder population, i.e., so as to determinewhether the bank will extend a credit line to a responder.

Accordingly, as shown in FIG. 1, score_1 is the score built to predictobjective one and score_2 is the score built to predict objective two.In this embodiment, we can find the best parameter to combine the twoscores by solving an optimization problem.

That is, after performing the steps 122 and 124, the process of FIG. 1passes to step 130. In step 130, a combination score is generated usingan optimization process as described below. The optimization processresults in a new combination score, which is output in step 160.

After step 160, the process of FIG. 1 passes to step 170. In step 170,the process ends.

FIG. 2 is a flow chart showing further details of the “generatecombination score using optimization process step” of FIG. 1 inaccordance with one embodiment of the invention. As shown in FIG. 2, theprocess starts in step 130, and passes to step both of steps 132 and134.

In step 132, the subprocess of FIG. 2 inputs the front end risk score,score_2. In parallel with step 132, in step 134, the process inputs theback end risk score, score_2. After each of steps 132 and 134, as shownin FIG. 2, the process passes to step 140.

In step 140, the optimization process is initiated. That is, in thisembodiment, the value of N is assigned 1,000, a counter “i” is assigneda value of 1, and α is assigned a value of zero. In other words, initialvalues are assigned to variables in order to initiate the iterativeoptimization process. The values of “i” and “N” control the progressionthrough and the termination of the iterative process. After step 140,the process passes to step 142.

As shown in FIG. 2, the variables are as follows:

β is a weighting factor or value that is generated for consideration inthe optimization process. β might be generated by a random numbergenerator in some controlled manner;

α is a weighting factor or value that represents the best weightingfactor achieved at a particular point in the progression of theoptimization process. α might be initiated at zero (0), for example;

“i” is the progressing integer value, i.e., a counter, that controls theprogression of generating the β values. For example, “i” might be 1, 2,3, 4 . . . 1000;

N is the value to which “i” will progress, e.g., 1000;

C1 is a benchmark score to determine if, in terms of Objective 1, β is afeasible value;

C2 is a benchmark score to determine if, in terms of Objective 2, β is afeasible value; and

KS is a Kolmogorov-Smirnov statistic.

Hereinafter, further general aspects of the systems and methods ofembodiments will be described in further detail. In this embodiment asdescribed above, first, the process develops each score based on its ownpopulation and objective function, using the same set of bureauvariables, or other suitable variables. Then, an optimization process isused to combine these two scores into a final product, i.e., a unifiedscore. The goal is the final score should outperform existing individualscore on its own population at same time and maximizes the totalperformance measurement. In this embodiment, it is assumed that score_1is the score built to predict objective one and score_2 is the scorebuilt to predict objective two. In this embodiment, the invention findsthe best parameter to combine the two scores by solving the followingoptimization problem:

Maximize(KS(SCR_(—)1+α*SCR_(—)2, objective one)+KS(SCR_(—)1+α*SCR_(—)2,objective two))

Subject to:

KS(SCR _(—)1+α*SCR _(—)2, objective one)>C1; AND

KS(SCR _(—)1+α*SCR _(—)2, objective two)>C2

Here the benchmark scores C1 and C2 are predetermined by theperformances of benchmark scores and business requirements, or otherparameters as may be desired. As a further step, adverse action reasoncodes may be generated from the same pool of model attributes by usingappropriate weights. That is, an adverse action reason code may beneeded to provide a reason why a responder (who was initially extendedan offer) was declined, i.e., subsequent to responding to the offer.Accordingly, the optimization process (in conjunction with generatingthe unified value) may also be manipulated so as to provide someintelligence regarding why a respondent might be declined (i.e., adverseaction reason codes).

With the approaches described above, the invention achieves the goal ofobtaining a unified score by taking advantages out of both scores. Ingeneral, it is appreciated that the idea of the invention may be used todevelop models to achieve satisfactory result for multiple goals at thesame time. The invention might be used in a variety of business and/orfor other objectives.

Returning now to FIG. 2, after step 140 in which the optimizationprocess is initiated, the process passes to step 142. In step 142, a βvalue is generated. The β value is generated in some suitable manner,i.e., such as using a random number generator. Various known techniquesmight be used such as a Monte Carlo approach and/or stratification ofthe β values. Accordingly, in some suitable manner, β is generated instep 142. As shown, β may be constrained to be between 1 and 10.

As shown in step 142, β is generated so as to be used in the KSstatistic, and specifically in the parameter:

score_(—)1+β*score_(—)2.

After step 142, the process passes to step 144. Step 144 might becharacterized as a determination of whether the current value of β isfeasible. Such feasibility is determined vis-à-vis benchmark scores C1and C2. That is, in step 142, the process determines if the KS statisticbased on β is satisfied vis-à-vis C1 and C2, i.e., the processdetermines if:

KS(score_(—)1+β*score_(—)2, Objective 1)>C1 AND

KS(score_(—)1+β*score_(—)2, Objective 2)>C2

If such two relationships are not satisfied, such is indicative that thecurrent value of β (i.e., the current weighting of the first and secondscores) is simply not feasible. Accordingly, the process of FIG. 2passes from step 144 to step 152. In step 152, the value of “i” isincremented by “1”, i.e., to count of one iteration. Then the processpasses to step 154 to determine if another iteration should beperformed, i.e., if the value of N has been attained by “i”. If thevalue of N has not been attained, the process passes back to step 142for another iteration.

Accordingly, in step 142 another β value is generated, e.g., using arandom number generator. The process will then again proceed to step 144to determine if the new value of β is feasible, i.e., to determine ifthe new value of β satisfies the criteria of step 144. Processing willthen proceed as discussed above.

In step 144, if a particular value of β satisfies the benchmarkcriteria, the process passes to step 146. Step 146 might becharacterized as presenting a challenger β that is compared with theexisting champion α. That is, the total KS statistic of(score_1+β*score_2, Objective 1) plus the KS statistic of(score_1+β*score_2, Objective 2) is determined. This KS statistic iscompared with the KS statistic of (score_1+α*score_2, Objective 1) plusthe KS statistic of (score_1+α*score_2, Objective 2). In other words,the KS statistic based on β is compared with the KS statistic based onα.

In step 146, if the KS statistic based on β is “less” than the KSstatistic based on α, i.e., the relationship of step 146 is notsatisfied, than the existing α remains the best weighting parameter.Accordingly, the process of FIG. 2 passes to step 152, wherein the valueof “i” is incremented, and processing proceeds as discussed above.

In this comparison of step 146, the process determines if the KSstatistic based on β is “less” than the KS statistic based on α.However, in short, any comparison or other process may be used todetermine if β is an optimized result over the existing α.

Returning to the processing of step 146, the relationship of step 146may be satisfied, i.e., β is better than the existing α. As a result,the process passes to step 150. In step 150, the value of β is assignedto α, which might be characterized as β becoming the new champion.

After step 150, the process passes to step 152. In step 152, the valueof “i” is incremented by 1. After step 152, the process passes to step154.

As described above, in step 154, the process determines whether anotheriterative loop should be effected, or whether the optimization processis completed. That is, in step 154 in this embodiment, the processdetermines whether the value of “i” is greater than “N”. If yes, thenall the iterative loops have been performed, and the optimizationprocess is complete. Accordingly, the process passes to step 156 and thecurrent α value constitutes the combination score. That is, thecombination score may be expressed as: “score_1+α*score_2”.

After step 156, the process passes to step 158. In step 158, the processreturns to step 160 of FIG. 1.

In summary of the processing of FIG. 1 and FIG. 2, a marketing campaignmay be based on Objective 1 (for example, indicator of bureau bad atmonth 12) and Objective 2 (for example, indicator of internal bad atmonth 12). As illustrated in FIG. 2, first, we build a score (score_1)to predict objective 1 on the whole campaign file and a score (score_2)to predict objective 2 on the approvals, i.e., the approvals beingrespondents who will be approved for a credit line.

As set forth above, the targeted KS (Kolmogorov-Smirnov) Statistics inpredicting objective 1 and objective 2 are C1 and C2, respectively. Theperformances of existing benchmark scores and the cost of thedevelopment and implementation of a new score may be used topredetermine C1 and C2. Through the optimization process, the finalscore has the form of (score_1 +α*score_2), where α is weightingparameter.

In the example of FIG. 2, a step-wise maximization method is used tofind the optimal value of α. That is, the value of “i” is stepped up asthe optimization process is progressed. However, it should of course beappreciated that other approaches may be used instead of a stepapproach. That is, any suitable methodology may be used to progressthrough the iterative processing and/or to choose the trail weightingvalues. For example, iterations might continue until some desiredcriteria is satisfied, e.g., criteria vis-à-vis the benchmark scores,for example.

Accordingly, FIGS. 1 and 2 set forth a process in accordance with oneembodiment of the invention. It should be appreciated that a suitableprocessing system is used to implement the processing of FIGS. 1 and 2.FIG. 3 is a block diagram showing a score processing system inaccordance with one embodiment of the invention.

As shown in FIG. 3, the score processing system 200 includes a firstscore processor 210 and a second score processor 220. The first scoreprocessor 210 generates a first score as illustratively shown in step122 of FIG. 1. The second score processor 220 generates a second scoreas illustratively shown in step 124 of FIG. 1. As described above, thefirst score and the second score are respectively related to desiredobjectives.

The score processing system 200 further includes a combination scoreprocessor 230. The combination score processor 230 may be used toperform the processing of step 130 of FIG. 1, i.e., the processing ofFIG. 2. The score processing system 200 further includes a datainput/output portion 260. The data input/output portion 260 is utilizedto input the various data used by the score processing system 200, aswell as to output data generated by the score processing system 200. Forexample, the data input/output portion 260 may include a user interfacethat allows a human user to interface with the score processing system200.

The score processing system 200 as shown in FIG. 3 further includes asystem memory portion 270. The system memory portion 270 is a generalpurpose memory to store any of a wide variety of data input into orgenerated by the score processing system 200.

FIG. 4 is a block diagram showing further details of the combinationscore processor 230 in accordance with one embodiment of the invention.The combination score processor 230 includes an iteration controlportion 232, a feasibility determination portion 234 and a comparisonportion 236.

With illustrative reference to FIG. 2, the feasibility determinationportion 234 performs the processing of step 144. That is, in accordancewith one embodiment, the feasibility determination portion 234determines if the particular weighting (e.g., the value of β) of thefront end risk score and the back end risk score is feasible.

If the weighting (β) is feasible, the process is then handed to thecomparison portion 236. The comparison portion 236 performs theprocessing of step 146, in the embodiment of FIG. 2. That is, thecomparison portion 236 determines if the value of the weighting factor β(the challenger) provides a better result than α (the current champion).

The combination score processor 230 also includes the iteration controlportion 232. The iteration control portion 232 controls the iterativeprocessing of FIG. 2. That is, for example, the iteration controlportion 232 performs steps 140, 150, 152 and 154 that relate to theiterative processing.

In further explanation of aspects of the invention, FIG. 5 is aflowchart showing a process of building a unified determination based onsubdeterminations in accordance with one embodiment of the invention. Insummary, FIG. 5 is a generalized optimization process vis-à-vis theprocessing of FIGS. 1 and 2. As shown, the process of FIG. 5 starts instep 500 and passes to step 512 and step 514, which may be performed inparallel.

In step 512, the process formulates a first subdetermination (e.g.,score) based on first criteria. In step 514, the process formulates asecond subdetermination (e.g., score) based on second criteria. Afterthe processing of steps 512 and 514 are performed, the process passes tostep 530.

In step 530, the process generates a unified determination (based on thefirst subdetermination and the second subdetermination) using anoptimization process. Further details of the processing of step 530 arediscussed below.

Then, in step 560, the process outputs a unified determination, e.g., aweighting value of (α). After step 560, the process passes to step 570,at which point the process ends.

FIG. 6 is a flow chart showing further details of the “generate theunified determination (based on the first subdetermination and thesecond subdetermination) using an optimization process” step 530 of FIG.5 in accordance with one embodiment of the invention.

After the subprocess starts in step 530 of FIG. 6, the process passes tosteps 532 and 534, i.e., steps 532 and 534 are performed in parallel. Instep 532, the process inputs the first subdetermination (e.g. score),which was generated in the processing of FIG. 5. In step 534, theprocess inputs the second subdetermination (e.g., score), which was alsogenerated in the processing of FIG. 5.

After steps 532 and 534, the process passes to step 540. In step 540,the process initiates iterations of the optimization process byrestarting a counter (i.e., that controls progression through theiteration process) and resets the a value to zero. After step 540, theprocess passes to step 542.

In step 542, the process generates a weighting factor β, and generates avalue based on the selected weighting factor, i.e., generates a resultfrom using β. For example, this might be performed usingKolmogorov-Smirnov statistic processing. Then, the process passes tostep 544. In step 544, the process determines whether the selectedweighting factor β satisfies the threshold criteria? If no, then theparticular β value is not feasible, and the process passes to step 554.

On the other hand, if the value of β in step 544 does indeed satisfy thethreshold criteria, then the process passes to step 546. In step 546,the process determines whether the selected weighting factor β providesan optimized result vis-à-vis a previous weighting, i.e., α (theweighting that was previously determined to be the optimized weighting,if any). If β is not a better result than the prior best result α, thenthe process passes to step 554.

Alternatively, if the value of β does provide a better result than thepreviously determined α, then the process passes to step 550. In step550, the β value is assigned to α. Then, the process passes to step 554.

In step 554, the process determines if the number of iterations thathave been performed exceed the total number of desired iterations to beperformed? For example, this may be accomplished using a suitablecounter, which is incremented by 1 until a threshold value is attained.If the process determines in step 554 that further iterations should beperformed, then the process passes to step 552. In step 552, the processdetermines the next weighting to try, e.g., using a random numbergenerator. The process then returns to step 542, and processingcontinues, as described above.

Alternatively, it may be the case that all the iterations have beenperformed. Accordingly, the current value of α is the optimizedweighting, as reflected in step 556. After step 556, the process passesto step 558. In step 558, the process returns to step 560 of FIG. 5.

The systems and methods of embodiments have been described above asrelating to a front end risk score and a backend risk score, i.e., ascore used in deciding who to mail offers to and a score in laterdeciding whether to indeed extend a line of credit to a responder.However, the invention is not limited to such application, Rather, theinvention extends to any situation in which respective scores, or otherdeterminations, are made under different circumstances, and it isdesired that a unified score be developed that best represents the tworespective scores. In particular, the invention might be used insituations where there is (a) a decision process as to whether an offerwill be extended; and later (b) a second decision process as to actiontaken with regard to such offer. Such processing is of course widelyapplicable to the financial arena.

Further, the systems and methods of embodiments are not in any waylimited to credit cards. Rather, the invention might be used inconjunction with a variety of financial mechanisms, in contractualsituations, or in any other situation where a unified relationship isdesired between two relationships.

Embodiments have been described above as relating to individuals, e.g.,a credit card offer being extended to an individual. However, theinvention is certainly not limited to such application. The inventionmight be applied to a business, group of individuals or any otherentity.

Further, as noted above, embodiments of the invention do not need toutilize the KS (Kolmogorov-Smirnov) test. Rather, other statisticalapproaches might be utilized to determine the variance between aparticular trial weighting β vis-à-vis the first score and the secondscore, i.e., as determined in steps 512 and 514 of FIG. 5, for example.For example, alternative measures might be (1) GINI Coefficient or (2)Information Values (IV). In summary, the KS Test is a measure of maximumdistance between cumulative distributions of two populations. The KSTest was originally developed by Kolmogorov & Smirnove. The GiniCoefficient (Efficiency Index) test measures area between two cumulativedistributions of two populations. It was originally developed by CorradoGini. On the other hand, the Information Value (Kullback-LeiblerDistance) test measures the distance between distributions of twopopulations. This test was originally developed by Kullback & Leibler.These tests, as well as other statistical approaches might be utilizedin the processing of the invention.

Hereinafter, general aspects of implementation of embodiments will bedescribed. As described above, FIGS. 3 and 4 show one embodiment of thesystem of the invention. Further, FIGS. 1, 2, 5 and 6 show various stepsof one embodiment of the method of the invention. The system of theinvention or portions of the system of the invention may be in the formof a “processing machine,” such as a general purpose computer, forexample. As used herein, the term “processing machine” is to beunderstood to include at least one processor that uses at least onememory. The at least one memory stores a set of instructions. Theinstructions may be either permanently or temporarily stored in thememory or memories of the processing machine. The processor executes theinstructions that are stored in the memory or memories in order toprocess data. The set of instructions may include various instructionsthat perform a particular task or tasks, such as those tasks describedabove in the flowcharts. Such a set of instructions for performing aparticular task may be characterized as a program, software program, orsimply software.

As noted above, the processing machine executes the instructions thatare stored in the memory or memories to process data. This processing ofdata may be in response to commands by a user or users of the processingmachine, in response to previous processing, in response to a request byanother processing machine and/or any other input, for example.

As noted above, the processing machine used to implement the inventionmay be a general purpose computer. However, the processing machinedescribed above may also utilize any of a wide variety of othertechnologies including a special purpose computer, a computer systemincluding a microcomputer, mini-computer or mainframe for example, aprogrammed microprocessor, a micro-controller, a peripheral integratedcircuit element, a CSIC (Customer Specific Integrated Circuit) or ASIC(Application Specific Integrated Circuit) or other integrated circuit, alogic circuit, a digital signal processor, a programmable logic devicesuch as a FPGA, PLD, PLA or PAL, or any other device or arrangement ofdevices that is capable of implementing the steps of the process of theinvention.

It is appreciated that in order to practice the method of the inventionas described above, it is not necessary that the processors and/or thememories of the processing machine be physically located in the samegeographical place. That is, each of the processors and the memoriesused in the invention may be located in geographically distinctlocations and connected so as to communicate in any suitable manner.Additionally, it is appreciated that each of the processor and/or thememory may be composed of different physical pieces of equipment.Accordingly, it is not necessary that the processor be one single pieceof equipment in one location and that the memory be another single pieceof equipment in another location. That is, it is contemplated that theprocessor may be two pieces of equipment in two different physicallocations. The two distinct pieces of equipment may be connected in anysuitable manner. Additionally, the memory may include two or moreportions of memory in two or more physical locations.

To explain further, processing as described above is performed byvarious components and various memories. However, it is appreciated thatthe processing performed by two distinct components as described abovemay, in accordance with a further embodiment of the invention, beperformed by a single component. Further, the processing performed byone distinct component as described above may be performed by twodistinct components. In a similar manner, the memory storage performedby two distinct memory portions as described above may, in accordancewith a further embodiment of the invention, be performed by a singlememory portion. Further, the memory storage performed by one distinctmemory portion as described above may be performed by two memoryportions.

Further, various technologies may be used to provide communicationbetween the various processors and/or memories, as well as to allow theprocessors and/or the memories of the invention to communicate with anyother entity; i.e., so as to obtain further instructions or to accessand use remote memory stores, for example. Such technologies used toprovide such communication might include a network, the Internet,Intranet, Extranet, LAN, an Ethernet, or any client server system thatprovides communication, for example. Such communications technologiesmay use any suitable protocol such as TCP/IP, UDP, or OSI, for example.

As described above, a set of instructions is used in the processing ofthe invention. The set of instructions may be in the form of a programor software. The software may be in the form of system software orapplication software, for example. The software might also be in theform of a collection of separate programs, a program module within alarger program, or a portion of a program module, for example Thesoftware used might also include modular programming in the form ofobject oriented programming. The software tells the processing machinewhat to do with the data being processed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processing machine may read theinstructions. For example, the instructions that form a program may bein the form of a suitable programming language, which is converted tomachine language or object code to allow the processor or processors toread the instructions. That is, written lines of programming code orsource code, in a particular programming language, are converted tomachine language using a compiler, assembler or interpreter. The machinelanguage is binary coded machine instructions that are specific to aparticular type of processing machine, i.e., to a particular type ofcomputer, for example. The computer understands the machine language.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include assembly language, Ada, APL, Basic, C, C++,COBOL, dBase, Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX,Visual Basic, and/or JavaScript, for example. Further, it is notnecessary that a single type of instructions or single programminglanguage be utilized in conjunction with the operation of the system andmethod of the invention. Rather, any number of different programminglanguages may be utilized as is necessary or desirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data. Further,files or other data may be decrypted using a suitable decryption module,for example.

As described above, the invention may illustratively be embodied in theform of a processing machine, including a computer or computer system,for example, that includes at least one memory. It is to be appreciatedthat the set of instructions, i.e., the software for example, thatenables the computer operating system to perform the operationsdescribed above may be contained on any of a wide variety of media ormedium, as desired. Further, the data that is processed by the set ofinstructions might also be contained on any of a wide variety of mediaor medium. That is, the particular medium, i.e., the memory in theprocessing machine, utilized to hold or implement the set ofinstructions and/or the data used in the invention may take on any of avariety of physical forms or transmissions, for example. Illustratively,the medium may be in the form of paper, paper transparencies, a compactdisk, a DVD, an integrated circuit, a hard disk, a floppy disk, anoptical disk, a magnetic tape, a RAM, a ROM, a PROM, a EPROM, a wire, acable, a fiber, communications channel, a satellite transmissions orother remote transmission, as well as any other medium or source of datathat may be read by the processors of the invention.

Further, the memory or memories used in the processing machine thatimplements the invention may be in any of a wide variety of forms toallow the memory to hold instructions, data, or other information, as isdesired. Thus, the memory might be in the form of a database to holddata. The database might use any desired arrangement of files such as aflat file arrangement or a relational database arrangement, for example.

In the system and method of the invention, a variety of “userinterfaces” may be utilized to allow a user to interface with theprocessing machine or machines that are used to implement the invention.As used herein, a user interface includes any hardware, software, orcombination of hardware and software used by the processing machine thatallows a user to interact with the processing machine. A user interfacemay be in the form of a dialogue screen for example. A user interfacemay also include any of a mouse, touch screen, keyboard, voice reader,voice recognizer, dialogue screen, menu box, list, checkbox, toggleswitch, a pushbutton or any other device that allows a user to receiveinformation regarding the operation of the processing machine as itprocesses a set of instructions and/or provide the processing machinewith information. Accordingly, the user interface is any device thatprovides communication between a user and a processing machine. Theinformation provided by the user to the processing machine through theuser interface may be in the form of a command, a selection of data, orsome other input, for example.

As discussed above, a user interface is utilized by the processingmachine that performs a set of instructions such that the processingmachine processes data for a user. The user interface is typically usedby the processing machine for interacting with a user either to conveyinformation or receive information from the user. However, it should beappreciated that in accordance with some embodiments of the system andmethod of the invention, it is not necessary that a human user actuallyinteract with a user interface used by the processing machine of theinvention. Rather, it is contemplated that the user interface of theinvention might interact, i.e., convey and receive information, withanother processing machine, rather than a human user. Accordingly, theother processing machine might be characterized as a user. Further, itis contemplated that a user interface utilized in the system and methodof the invention may interact partially with another processing machineor processing machines, while also interacting partially with a humanuser.

It will be readily understood by those persons skilled in the art thatthe present invention is susceptible to broad utility and application.Many embodiments and adaptations of the present invention other thanthose herein described, as well as many variations, modifications andequivalent arrangements, will be apparent from or reasonably suggestedby the present invention and foregoing description thereof, withoutdeparting from the substance or scope of the invention.

Accordingly, while the present invention has been described here indetail in relation to its exemplary embodiments, it is to be understoodthat this disclosure is only illustrative and exemplary of the presentinvention and is made to provide an enabling disclosure of theinvention. Accordingly, the foregoing disclosure is not intended to beconstrued or to limit the present invention or otherwise to exclude anyother such embodiments, adaptations, variations, modifications orequivalent arrangements.

1-23. (canceled)
 24. A system for generating a unified determinationbased on subdeterminations, the system including: a first scoreprocessor generating a first subdetermination based on first criteria,wherein the first criteria is a front end risk score; a second scoreprocessor generating a second subdetermination based on second criteria,wherein the second criteria is a back end risk score; a combinationscore processor generating a unified determination based on the firstsubdetermination and the second subdetermination, the combination scoreprocessor including: an iteration control portion assigning, usingiterative processing, an assigned weighting respectively to the firstdetermination and second determination; a feasibility determinationportion determining if the assigned weighting satisfies at least oneconstraint; a comparison portion comparing the assigned weighting to anoptimized weighting, which was previously determined, to determine ifthe assigned weighting is improved over the optimized weighting; and ifthe assigned weighting is improved, then the comparison portionassigning the assigned weighting to be the optimized weighting; whereinthe first subdetermination is a first score, and the secondsubdetermination is a second score; wherein the feasibilitydetermination portion determining if the assigned weighting satisfies atleast one constraint is performed using a first relationship includingparameters, and comparing a result of the first relationship with afirst constraint value; wherein the determining if the assignedweighting satisfies at least one constraint is performed using a secondrelationship including parameters, and comparing a result of the secondrelationship with a second constraint value; and wherein the comparisonportion comparing the assigned weighting to an optimized weighting todetermine if the assigned weighting is improved over the optimizedweighting includes: using a first relationship including firstparameters to generate a first result, the first parameters includingthe first subdetermination, the second subdetermination, the assignedweighting and a first objective parameter; and using a secondrelationship including second parameters to generate a second result,the second parameters including the first subdetermination, the secondsubdetermination, the optimized weighting and the first objectiveparameter, and comparing the first result to the second result; and thesystem constituted by a tangibly embodied computer processing machine.25. The system of claim 24, the parameters of the first relationshipincluding the first subdetermination, the second subdetermination, theassigned weighting and a first objective parameter,
 26. The system ofclaim 25, the parameters of the second relationship including the firstsubdetermination, the second subdetermination, the assigned weightingand a second objective parameter,
 27. The system of claim 24, theparameters of the second relationship including the firstsubdetermination, the second subdetermination, the assigned weightingand a second objective parameter,
 28. The system of claim 24, the frontend risk score determines if an individual is be mailed an offer for afinancial product; and the back end risk score determines if theindividual, who is a respondent to the offer, is to be extended thefinancial product.
 29. The system of claim 28, wherein the financialproduct is a credit card.
 30. The system of claim 28, further includingthe system: inputting information from a credit bureau entity; andgenerating the first score and the second score based on the informationinput from the credit bureau entity.
 31. The system of claim 30,including the system securing information from an Applicant for a creditcard; and the system using the information from the Applicant for acredit card to generate the second score.
 32. The system of claim 24,wherein the first result and the second result are computed by thesystem using a Kolmogorov-Smirnov statistic.
 33. The system of claim 24,further including the system using a second objective parameter indetermining the first result and the second result.
 34. The system ofclaim 24, wherein the iterative processing is effected by the systemusing a progressing value, the progressing value being advanced after(a) the determining if the assigned weighting satisfies at least oneconstraint, and (b) comparing the assigned weighting to an optimizedweighting.
 35. The system of claim 34, wherein the progressing value isadvanced until the progressing value attains a threshold value, whichmarks the final iteration.
 36. The system of claim 34, wherein theassigning, using iterative processing, the assigned weightingrespectively to the first determination and second determinationincludes utilizing a random number generator to assign the weighting.